This paper presents a robust adaptive repetitive control (RARC) method for a class of periodically time-varying nonlinear systems with aperiodic uncertainties. A sigma modification is introduced in the learning algori...
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This paper presents a robust adaptive repetitive control (RARC) method for a class of periodically time-varying nonlinear systems with aperiodic uncertainties. A sigma modification is introduced in the learning algorithm of RARC, in order to guarantee robustness of the system undertaken. The closed-loop type learning algorithm is examined and it is shown that the realisability cannot be assured when the sigma modification is applied. To avoid the causality contradiction, an open-loop type learning algorithm with switching sigma modification is proposed to guarantee robustness and achieve the asymptotic convergence of the tracking error, when the disturbances disappear. Extension to the RARC for robotic manipulators is given and the numerical simulation is carried out to verify the effectiveness of the learning control scheme.
This paper presents a robust adaptive repetitive control(RARC) method for trajectory tracking of uncertain robotic manipulators. repetitivecontrol is applied for periodic trajectory tracking and a sigma modification ...
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ISBN:
(纸本)9781728159225
This paper presents a robust adaptive repetitive control(RARC) method for trajectory tracking of uncertain robotic manipulators. repetitivecontrol is applied for periodic trajectory tracking and a sigma modification is introduced in the periodic learning laws to guarantee the robustness of the system. All the signals in the closed loop are proved to be bounded. An open-loop learning algorithm with switching s modification is designed to achieve asymptotic convergence of the tracking errors when the disturbances disappear. The simulation is made to show the effectiveness of the algorithms.
There exist nonlinearities, uncertainties and time-varying characteristics in the system dynamics of manipulators driven by pneumatic artificial muscles (PAMs), which makes the accurate dynamic modeling and controller...
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There exist nonlinearities, uncertainties and time-varying characteristics in the system dynamics of manipulators driven by pneumatic artificial muscles (PAMs), which makes the accurate dynamic modeling and controller design challenging. In this paper, a robust adaptive repetitive control scheme is proposed to solve the periodic-trajectory tracking problem for a one-degree of freedom (one-DOF) manipulator driven by two PAMs. First, the system model of the one-DOF PAM-driven manipulator is analyzed and derived. Next, during output constraint design by using a barrier Lyapunov function, a sliding mode surface is reasonably constructed to compensate for the differential term of time-varying constraint parameter. Then, with the help of Lipchitz condition, signal replacement technique and reparameterization are implemented to deal with nonparametric uncertainties in the manipulator system for the subsequent uncertainty compensation by using difference learning method and robust feedback strategy. Moreover, the stability of closed-loop PAM-driven manipulator is proven theoretically by using Lyapunov synthesis. In the end, comparative experiments are provided on a self-built PAM testbed to demonstrated the effectiveness of the proposed robust adaptive repetitive control scheme.
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